Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis

By Jing Tang, Agnieszka Szwajda, Sushil Kumar Shakyawar, Tao Xu, P. Hintsanen, Krister Wennerberg, Tero Aittokallio. Journal of Chemical Information and Modeling, 2014

601 citations recorded by the source corpus.

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/17c0f710bc4e8c4d88f2163438fa2549a384368e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Pharmacology

Citations in the corpus

SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, methodology): “An approach to integrate observations from different sources, named KIBA , and a corresponding dataset are presented in [42].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “DeepDTA is optimized for Davis [25] and KIBA [26] dataset which contains kinases protein, their inhibitors, and dense affinity values, showing better prediction performances than previous affinity prediction models.”
Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model (2020, methodology): “Neverthless, MT-DTI showed the best performance [12] when compared to a deep learning-based (DeepDTA) approach [21] and two traditional machine learning-based algorithms SimBoost [22], and KronRLS [23], with the KIBA [24] and DAVIS [25] data sets.”
Toward more realistic drug–target interaction predictions (2014, unknown): “Already some of the earlier works considered the different scenarios where either the drug or the target in the drug–target pair to be predicted is not prediction performance have been considered either alone or together with the different experimental settings.”
Deep learning in drug discovery: an integrative review and future challenges (2022, background): “2011) and the KIBA dataset (Sun et al.”
DeepDTA: deep drug–target binding affinity prediction (2018, methodology): “Experiment Setup We evaluated the performance of the proposed model on the benchmark data sets [17, 54] similarly to [29].”
Pre-training Molecular Graph Representation with 3D Geometry (2021, unknown): “1, 2, 4, 6, 17, 24 [83] Jing Tang, Agnieszka Szwajda, Sushil Shakyawar, Tao Xu, Petteri Hintsanen, Krister Wennerberg, and Tero Aittokallio.”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “Suggested data split: cold drug split, cold target split; Evaluation: MAE, Pearson Correlation; Unit: nM. TDC.KIBA: As various experimental assays have different units during experiments, Tang et al. (2014) propose KIBA score to aggregate the IC50, Kd, and Ki scores.”